2,768 research outputs found
Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention
Although face recognition has made impressive progress in recent years, we
ignore the racial bias of the recognition system when we pursue a high level of
accuracy. Previous work found that for different races, face recognition
networks focus on different facial regions, and the sensitive regions of
darker-skinned people are much smaller. Based on this discovery, we propose a
new de-bias method based on gradient attention, called Gradient Attention
Balance Network (GABN). Specifically, we use the gradient attention map (GAM)
of the face recognition network to track the sensitive facial regions and make
the GAMs of different races tend to be consistent through adversarial learning.
This method mitigates the bias by making the network focus on similar facial
regions. In addition, we also use masks to erase the Top-N sensitive facial
regions, forcing the network to allocate its attention to a larger facial
region. This method expands the sensitive region of darker-skinned people and
further reduces the gap between GAM of darker-skinned people and GAM of
Caucasians. Extensive experiments show that GABN successfully mitigates racial
bias in face recognition and learns more balanced performance for people of
different races.Comment: Accepted by CVPR 2023 worksho
Balancing Biases and Preserving Privacy on Balanced Faces in the Wild
Demographic biases exist in current models used for facial recognition (FR).
Our Balanced Faces in the Wild (BFW) dataset is a proxy to measure bias across
ethnicity and gender subgroups, allowing one to characterize FR performances
per subgroup. We show that results are non-optimal when a single score
threshold determines whether sample pairs are genuine or imposters.
Furthermore, within subgroups, performance often varies significantly from the
global average. Thus, specific error rates only hold for populations matching
the validation data. We mitigate the imbalanced performances using a novel
domain adaptation learning scheme on the facial features extracted from
state-of-the-art neural networks, boosting the average performance. The
proposed method also preserves identity information while removing demographic
knowledge. The removal of demographic knowledge prevents potential biases from
being injected into decision-making and protects privacy since demographic
information is no longer available. We explore the proposed method and show
that subgroup classifiers can no longer learn from the features projected using
our domain adaptation scheme. For source code and data, see
https://github.com/visionjo/facerec-bias-bfw.Comment: arXiv admin note: text overlap with arXiv:2102.0894
Improving fairness in machine learning systems: What do industry practitioners need?
The potential for machine learning (ML) systems to amplify social inequities
and unfairness is receiving increasing popular and academic attention. A surge
of recent work has focused on the development of algorithmic tools to assess
and mitigate such unfairness. If these tools are to have a positive impact on
industry practice, however, it is crucial that their design be informed by an
understanding of real-world needs. Through 35 semi-structured interviews and an
anonymous survey of 267 ML practitioners, we conduct the first systematic
investigation of commercial product teams' challenges and needs for support in
developing fairer ML systems. We identify areas of alignment and disconnect
between the challenges faced by industry practitioners and solutions proposed
in the fair ML research literature. Based on these findings, we highlight
directions for future ML and HCI research that will better address industry
practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in
Computing Systems (CHI 2019
Survey of Social Bias in Vision-Language Models
In recent years, the rapid advancement of machine learning (ML) models,
particularly transformer-based pre-trained models, has revolutionized Natural
Language Processing (NLP) and Computer Vision (CV) fields. However, researchers
have discovered that these models can inadvertently capture and reinforce
social biases present in their training datasets, leading to potential social
harms, such as uneven resource allocation and unfair representation of specific
social groups. Addressing these biases and ensuring fairness in artificial
intelligence (AI) systems has become a critical concern in the ML community.
The recent introduction of pre-trained vision-and-language (VL) models in the
emerging multimodal field demands attention to the potential social biases
present in these models as well. Although VL models are susceptible to social
bias, there is a limited understanding compared to the extensive discussions on
bias in NLP and CV. This survey aims to provide researchers with a high-level
insight into the similarities and differences of social bias studies in
pre-trained models across NLP, CV, and VL. By examining these perspectives, the
survey aims to offer valuable guidelines on how to approach and mitigate social
bias in both unimodal and multimodal settings. The findings and recommendations
presented here can benefit the ML community, fostering the development of
fairer and non-biased AI models in various applications and research endeavors
KFC: Kinship Verification with Fair Contrastive Loss and Multi-Task Learning
Kinship verification is an emerging task in computer vision with multiple
potential applications. However, there's no large enough kinship dataset to
train a representative and robust model, which is a limitation for achieving
better performance. Moreover, face verification is known to exhibit bias, which
has not been dealt with by previous kinship verification works and sometimes
even results in serious issues. So we first combine existing kinship datasets
and label each identity with the correct race in order to take race information
into consideration and provide a larger and complete dataset, called KinRace
dataset. Secondly, we propose a multi-task learning model structure with
attention module to enhance accuracy, which surpasses state-of-the-art
performance. Lastly, our fairness-aware contrastive loss function with
adversarial learning greatly mitigates racial bias. We introduce a debias term
into traditional contrastive loss and implement gradient reverse in race
classification task, which is an innovative idea to mix two fairness methods to
alleviate bias. Exhaustive experimental evaluation demonstrates the
effectiveness and superior performance of the proposed KFC in both standard
deviation and accuracy at the same time.Comment: Accepted by BMVC 202
MixFairFace: Towards Ultimate Fairness via MixFair Adapter in Face Recognition
Although significant progress has been made in face recognition, demographic
bias still exists in face recognition systems. For instance, it usually happens
that the face recognition performance for a certain demographic group is lower
than the others. In this paper, we propose MixFairFace framework to improve the
fairness in face recognition models. First of all, we argue that the commonly
used attribute-based fairness metric is not appropriate for face recognition. A
face recognition system can only be considered fair while every person has a
close performance. Hence, we propose a new evaluation protocol to fairly
evaluate the fairness performance of different approaches. Different from
previous approaches that require sensitive attribute labels such as race and
gender for reducing the demographic bias, we aim at addressing the identity
bias in face representation, i.e., the performance inconsistency between
different identities, without the need for sensitive attribute labels. To this
end, we propose MixFair Adapter to determine and reduce the identity bias of
training samples. Our extensive experiments demonstrate that our MixFairFace
approach achieves state-of-the-art fairness performance on all benchmark
datasets.Comment: Accepted in AAAI-23; Code: https://github.com/fuenwang/MixFairFac
Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific Experts
Bias mitigation in image classification has been widely researched, and
existing methods have yielded notable results. However, most of these methods
implicitly assume that a given image contains only one type of known or unknown
bias, failing to consider the complexities of real-world biases. We introduce a
more challenging scenario, agnostic biases mitigation, aiming at bias removal
regardless of whether the type of bias or the number of types is unknown in the
datasets. To address this difficult task, we present the Partition-and-Debias
(PnD) method that uses a mixture of biases-specific experts to implicitly
divide the bias space into multiple subspaces and a gating module to find a
consensus among experts to achieve debiased classification. Experiments on both
public and constructed benchmarks demonstrated the efficacy of the PnD. Code is
available at: https://github.com/Jiaxuan-Li/PnD.Comment: ICCV 202
Bias in Deep Learning and Applications to Face Analysis
Deep learning has fostered the progress in the field of face analysis, resulting in the integration of these models in multiple aspects of society. Even though the majority of research has focused on optimizing standard evaluation metrics, recent work has exposed the bias of such algorithms as well as the dangers of their unaccountable utilization.n this thesis, we explore the bias of deep learning models in the discriminative and the generative setting. We begin by investigating the bias of face analysis models with regards to different demographics. To this end, we collect KANFace, a large-scale video and image dataset of faces captured ``in-the-wild’'. The rich set of annotations allows us to expose the demographic bias of deep learning models, which we mitigate by utilizing adversarial learning to debias the deep representations. Furthermore, we explore neural augmentation as a strategy towards training fair classifiers. We propose a style-based multi-attribute transfer framework that is able to synthesize photo-realistic faces of the underrepresented demographics. This is achieved by introducing a multi-attribute extension to Adaptive Instance Normalisation that captures the multiplicative interactions between the representations of different attributes. Focusing on bias in gender recognition, we showcase the efficacy of the framework in training classifiers that are more fair compared to generative and fairness-aware methods.In the second part, we focus on bias in deep generative models. In particular, we start by studying the generalization of generative models on images of unseen attribute combinations. To this end, we extend the conditional Variational Autoencoder by introducing a multilinear conditioning framework. The proposed method is able to synthesize unseen attribute combinations by modeling the multiplicative interactions between the attributes. Lastly, in order to control protected attributes, we investigate controlled image generation without training on a labelled dataset. We leverage pre-trained Generative Adversarial Networks that are trained in an unsupervised fashion and exploit the clustering that occurs in the representation space of intermediate layers of the generator. We show that these clusters capture semantic attribute information and condition image synthesis on the cluster assignment using Implicit Maximum Likelihood Estimation.Open Acces
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